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临床试验/NCT04490343
NCT04490343Unknown不适用

Detection of Urinary Tract Stones on Ultra-low Dose Abdominopelvic CT Imaging With Deep-learning Image Reconstruction Algorithm

Centre Hospitalier Universitaire, Amiens2 个研究点 分布在 1 个国家目标入组 62 人开始时间: 2020年7月21日最近更新:
适应症

试验速览

阶段
不适用
入组人数
62
试验地点
2
主要终点
Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones

研究概览

简要总结

Urolithiasis has an increasing incidence and prevalence worldwide, and some patients may have multiple recurrences. Because these stone-related episodes may lead to multiple diagnostic examinations requiring ionizing radiation, urolithiasis is a natural target for dose reduction efforts. Abdominopelvic low dose CT, which has the highest sensitivity and specificity among available imaging modalities, is the most appropriate diagnostic exam for this pathology. The main objective of this study is to evaluate the diagnostic performance of ultra-low dose CT using deep learning-based reconstruction in urolithiasis patients.

研究设计

研究类型
Interventional
分配方式
Na
干预模型
Single Group
主要目的
Diagnostic
盲法
None

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Age ≥ 18 years old,
  • Patient referred for abdominopelvic CT to confirm urolithiasis or for follow-up,
  • Affiliation to a social security program,
  • Ability of the subject to understand and express opposition

排除标准

  • Age <18 years old,
  • Person under guardianship or curators,
  • Pregnant woman,
  • Any contraindications to CT

结局指标

主要结局

Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones

时间窗: day 1

Accuracy between low dose CT using DLIR reconstruction and low dose CT without DLIR reconstruction for the detection of urinary tract stones. Patients who were referred to the department for abdominopelvic CT exam for urolithiasis diagnostic or follow-up, and had consented to participate in the study, will undergo an additional ultra-low dose acquisition (ULD, \<1 mSv) with deep learning-based reconstruction (DLIR).

次要结局

未报告次要终点

研究者

申办方类型
Other
责任方
Sponsor

研究点 (2)

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